Geospatial Techniques for Social Scientists

.title[
# Geospatial Techniques for Social Scientists
]
.subtitle[
## Applied Data Wrangling & Linking
]
.author[
### Stefan Jünger
]
.institute[
### <p>Methods and Data Hub, Cluster of Excellence “The Politics of Inequality”</p>
]
.date[
### July 15, 2022
]

---

---

## Now

<table class="table" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Day </th>
   <th style="text-align:left;"> Time </th>
   <th style="text-align:left;"> Title </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;color: gray !important;"> July 14 </td>
   <td style="text-align:left;color: gray !important;"> 09:00am-10:30am </td>
   <td style="text-align:left;font-weight: bold;"> Introduction to GIS </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;"> July 14 </td>
   <td style="text-align:left;color: gray !important;"> 10:30am-12:00pm </td>
   <td style="text-align:left;font-weight: bold;"> Vector Data </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;color: gray !important;"> July 14 </td>
   <td style="text-align:left;color: gray !important;color: gray !important;"> 12:00pm-01:00pm </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Lunch Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;"> July 14 </td>
   <td style="text-align:left;color: gray !important;"> 01:00pm-02:30pm </td>
   <td style="text-align:left;font-weight: bold;"> Mapping </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;border-bottom: 1px solid"> July 14 </td>
   <td style="text-align:left;color: gray !important;border-bottom: 1px solid"> 02:30pm-04:00pm </td>
   <td style="text-align:left;font-weight: bold;border-bottom: 1px solid"> Raster Data </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;"> July 15 </td>
   <td style="text-align:left;color: gray !important;"> 09:00am-10:00am </td>
   <td style="text-align:left;font-weight: bold;"> Advanced Data Import &amp; Processing </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;background-color: yellow !important;"> July 15 </td>
   <td style="text-align:left;color: gray !important;background-color: yellow !important;"> 10:00am-11:00am </td>
   <td style="text-align:left;font-weight: bold;background-color: yellow !important;"> Applied Data Wrangling &amp; Linking </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;"> July 15 </td>
   <td style="text-align:left;color: gray !important;"> 11:00am-12:00pm </td>
   <td style="text-align:left;font-weight: bold;"> Investigating Spatial Autocorrelation </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;color: gray !important;"> July 15 </td>
   <td style="text-align:left;color: gray !important;color: gray !important;"> 12:00pm-01:00pm </td>
   <td style="text-align:left;font-weight: bold;color: gray !important;"> Lunch Break </td>
  </tr>
  <tr>
   <td style="text-align:left;color: gray !important;"> July 15 </td>
   <td style="text-align:left;color: gray !important;"> 01:00pm-02:45pm </td>
   <td style="text-align:left;font-weight: bold;"> Spatial Econometrics &amp; Outlook </td>
  </tr>
</tbody>
</table>

---

## What Are Georeferenced Data?

.pull-left[
</br>
Data with a direct spatial reference `$\rightarrow$` **geo-coordinates**
- Information about geometries
- Optional: Content in relation to the geometries
]

.pull-right[
<img src="data:image/png;base64,#../img/fig_geometries.png" width="85%" style="display: block; margin: auto;" />

.tinyisher[Sources: OpenStreetMap / GEOFABRIK (2018), City of Cologne (2014), and the Statistical Offices of the Federation and the Länder (2016) / Jünger, 2019]
]

---

## Georeferenced Survey Data

Survey data enriched with geo-coordinates (or other direct spatial references)

</br>

</br>

.center[**With georeferenced survey data, we can analyze interactions between individual behaviors and attitudes and the environment.**]

---

## Prerequisite: Geocoding

.pull-left[
Indirect spatial references have to be converted into direct spatial references
`$\rightarrow$` Addresses to geo-coordinates

Different service providers can be used
- e.g., Google, Bing, OSM
- In Germany: Federal Agency of Cartography and Geodesy (BKG)
]

.pull-right[
</br>
</br>
<img src="data:image/png;base64,#../img/geocoding.png" width="785" style="display: block; margin: auto;" />
]

---

## Georeferenced Survey Data `$\neq$` Geospatial Data

.pull-left[
We must not store geo-coordinates and survey data in one dataset
- Differences to geospatial data
- More complicated workflow to work with (see Challenges)
]

.pull-right[
<img src="data:image/png;base64,#../img/fig_linking_workflow_simple.png" width="85%" style="display: block; margin: auto;" />

---

## Data Availability

.pull-left[
Geospatial Data
- Often de-centralized distributed 
- Fragmented data landscape, at least in Germany

Georeferenced Survey Data
- Primarily, survey data
- Depends on documentation
- Access difficult due to data protection restrictions
]

.pull-right[
<img src="data:image/png;base64,#../img/data_availability.png" width="75%" style="display: block; margin: auto;" />
.right[.tinyisher[
https://www.eea.europa.eu/data-and-maps
https://datasearch.gesis.org/
https://datasetsearch.research.google.com/
]]
]

---

## Technical Procedures

.pull-left[
</br>
.center[<img src="./img/angry_cat.gif" width="75%">]
.center[.tinyisher[https://giphy.com/gifs/VbnUQpnihPSIgIXuZv]]
]

.pull-right[
Geocoding
- Reasonable automated procedure
- But differ in quality and access rights
- High risk for data protection

GIS procedures
- Requires exploiting specialized software
- Can get complex and resource-intensive
]

---

## Data Protection

</br>
</br>
That‘s one of the biggest issues
- Explicit spatial references increase the risk of re-identifying anonymized survey respondents
- Can occur during the processing of data but also during the analysis

</br>

---

## Legal Regulations

.pull-left[
Storing personal information such as addresses in the same place as actual survey attributes is not allowed in Germany
- Projects keep them in separate locations
- Can only be matched with a correspondence table
- Necessary to conduct data linking
]

.pull-right[
<img src="data:image/png;base64,#../img/fig_linking_workflow_simple.png" width="949" style="display: block; margin: auto;" />

---

## Distribution & Re-Identification Risk

Data may still be sensitive
- Geospatial attributes add new information to existing data
- May be part of general data privacy checks, but we may not distribute these data as is

.pull-right[
<img src="data:image/png;base64,#../img/safe_room.png" width="825" style="display: block; margin: auto;" />
.right[.tinyisher[https://www.gesis.org/en/services/processing-and-analyzing-data/guest-research-stays/secure-data-center-sdc]]
]

---

## Spatial Linking

.tinyisher[Sources:  OpenStreetMap / GEOFABRIK (2018), City of Cologne (2014), Leibniz Institute of Ecological Urban and Regional Development (2018), Statistical Offices of the Federation and the Länder (2016), and German Environmental Agency / EIONET Central Data Repository (2016) / Jünger, 2019]

---

## Spatial Linking Methods (Examples) I

1:1

<img src="data:image/png;base64,#../img/fig_linking_by_location_noise.png" width="75%" style="display: block; margin: auto;" />
]

Distances

<img src="data:image/png;base64,#../img/fig_linking_distance_noise_appI.png" width="75%" style="display: block; margin: auto;" />
]

.tinyisher[Sources: German Environmental Agency / EIONET Central Data Repository (2016) and OpenStreetMap / GEOFABRIK (2018) / Jünger, 2019]

---

## Spatial Linking Methods (Examples) II

Filter methods

]

Buffer zones

<img src="data:image/png;base64,#../img/fig_linking_buffer_sealing.png" width="75%" style="display: block; margin: auto;" />
]

.tinyisher[Sources: Leibniz Institute of Ecological Urban and Regional Development (2018) and Statistical Offices of the Federation and the Länder (2016) / Jünger, 2019]

---

## Environmental inequalities (Jünger, 2021)

> Is income associated with fewer environmental disadvantages, and are there differences between German people and people with a migration background?

.pull-left[
.small[
Theoretical Framework
- Social and Ethnic Inequalities (Crowder & Downey, 2010)
- Place Stratification (Lersch, 2013)

Data
- GGSS 2016 & 2018
- soil sealing & green spaces
]
]

.pull-right[
<img src="data:image/png;base64,#../img/fig_linking_buffer_sealing.png" width="65%" style="display: block; margin: auto;" />

---

## Results

.tinyisher[Data source: GGSS 2016 & 2018; N = 6,117; 95% confidence intervals based on cluster-robust standard errors (sample point); all models control for age, gender, education, household size, german region and survey year interaction, inhabitant size of the municipality, and distance to municipality administration]

---

## Attitudes towards minorities (Jünger & Schaeffer, 2022)

> Do people who live in ethnic homogenous neighborhoods that are close to ethnic diverse ones have more negative attitudes towards minorities?

.pull-left[
.small[
Theoretical Framework
- Contact Theory (Allport, 1954)
- Ethnic Competition (Stephan et al., 2009)

Data
- GGSS 2016
- German Census 2011
]
]

.pull-right[
<img src="data:image/png;base64,#../img/Abb1.png" width="65%" style="display: block; margin: auto;" />

---

## Results

.tinyisher[Data source: GGSS 2016; N = 1,689; 95% confidence intervals based on cluster-robust standard errors (sample point); all models control for age, gender, education, income, unemployment, homeownership, immigrants and inhabitants in the neighborhood, inhabitant size of the municipality, german region]

---

## Addon-slides: Simulated case study

---

## Fake Research Question

.pull-left[
Say we're interested in the impact of the current pandemic on individual well-being in the geographic context.

We plan to conduct a survey in the state of North-Rhine Westphalia and Baden Wurttemberg.
]

.pull-right[
</br>
<img src="data:image/png;base64,#../img/4iq3kg.jpg" width="813" style="display: block; margin: auto;" />
.center[.tinyisher[https://imgflip.com/memegenerator/Trump-Bill-Signing]
]
]

---

## Our Sample Area: NRW's and BW's Boundaries

```r
nrw <-
  osmdata::getbb(
    "Nordrhein-Westfalen", 
    format_out = "sf_polygon"
  ) %>% 
  .$multipolygon %>% 
  sf::st_transform(3035)

bw <-
  osmdata::getbb(
    "Baden-Württemberg", 
    format_out = "sf_polygon"
  ) %>% 
  .$multipolygon %>% 
  sf::st_transform(3035)

sampling_area <- 
  dplyr::bind_rows(
    nrw %>% dplyr::mutate(state = "nrw"), 
    bw %>% dplyr::mutate(state = "bw")
  )
```
]

```r
tm_shape(sampling_area) +
  tm_borders() 
```

<img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/nrw-map-1.png" style="display: block; margin: auto;" />
]

---

## A Fake-Life Application

We can draw a fake sample this way and also add an identifier for the respondents:

```r
set.seed(1234)
```

```r
fake_coordinates <-
  sf::st_sample(sampling_area, 1000) %>% 
  sf::st_sf() %>% 
  dplyr::mutate(
    id_2 = 
      stringi::stri_rand_strings(10000, 10) %>% 
      sample(1000, replace = FALSE)
  )
```
]

```r
tm_shape(sampling_area) +
  tm_borders() +
  tm_shape(fake_coordinates) +
  tm_dots()
```

<img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/map-osm-coordinates-1.png" style="display: block; margin: auto;" />
]

---

## Correspondence Table

As in any survey that deals with addresses, we need a correspondence table of the distinct identifiers.

```r
correspondence_table <-
  dplyr::bind_cols(
    id = 
      stringi::stri_rand_strings(10000, 10) %>% 
      sample(1000, replace = FALSE),
    id_2 = fake_coordinates$id_2
  )

correspondence_table
```

```
## # A tibble: 1,000 × 2
##    id         id_2      
##    <chr>      <chr>     
##  1 ZB7Z1ZZeXP O7fTi57jEz
##  2 yJvlYtinvl GR3DU6RNwC
##  3 eFfTgwO7q9 HIROndK7fZ
##  4 lKkUPbCnkQ r0II3cKoc6
##  5 HzCinC6PAv fffsx5Ka5Q
##  6 K2MFPDBd2Z q46L4y6sHb
##  7 31XssNjrKi vEMkSbk41Y
##  8 2mC4nCZ2xL QbFp2Km7CN
##  9 reRcF9A6jY xPovR3CEG5
## 10 tk2G0MsTU2 YFL4QpbWjx
## # … with 990 more rows
```

---

## Conduct the Survey

We ask respondents for some standard sociodemographics. But we also apply a new and highly innovative item score, called the Fake Corona Burden Score (FCBS) using the [`faux` package](https://cran.r-project.org/web/packages/faux/index.html).

```r
fake_survey_data <- 
  dplyr::bind_cols(
    id = correspondence_table$id,
    age = sample(18:100, 1000, replace = TRUE),
    gender = 
      sample(1:2, 1000, replace = TRUE) %>% 
      as.factor(),
    education =
      sample(1:4, 1000, replace = TRUE) %>% 
      as.factor(),
    income =
      sample(100:10000, 1000, replace = TRUE),
    fcbs = secret_variable_we_are_hiding_from_you
  )
```

---

## Survey Data Structure

```r
fake_survey_data 
```

```
## # A tibble: 1,000 × 6
##    id           age gender education income  fcbs
##    <chr>      <int> <fct>  <fct>      <int> <dbl>
##  1 ZB7Z1ZZeXP    83 1      2            246  44.9
##  2 yJvlYtinvl    38 2      3           1985  42.0
##  3 eFfTgwO7q9    30 1      4           8382  58.1
##  4 lKkUPbCnkQ    99 1      1           8133  53.5
##  5 HzCinC6PAv    49 2      1           1940  62.1
##  6 K2MFPDBd2Z    50 2      2           5134  68.2
##  7 31XssNjrKi    46 1      1           3884  49.2
##  8 2mC4nCZ2xL    84 1      3           2137  38.8
##  9 reRcF9A6jY    22 2      4           6441  58.9
## 10 tk2G0MsTU2    42 1      1           4547  56.7
## # … with 990 more rows
```

---

## What could explain our Fake Corona Burden Score?

*Likelihood to meet people*
> 1) The lower the district's population density, the lower the Fake Corona Burden Score.

*District relatively high affected by Covid-19*
> 2) If the Corona deaths in the district is higher than in the state, the lower the Fake Corona Burden Score.

---

## What could explain our Fake Corona Burden Score?

*Provision of health services*
> 3) The closer the next hospital to the respondent, the lower the Fake Corona Burden Score.

*Possible language issues in health care communication*
> 3) The higher the immigrant rate in the neighborhood, the higher the Fake Corona Burden Score.

---

## Variable Overview

Good Thing: We have all the data to calculate these indicators, even though we need first to transform some of the variables.

1. Calculate the variables at the district and state levels.
  2. Link the district and state-level data with the fake survey respondents.
  3. Calculate the hospital distance and immigrant rate.

---

## Population Density

When all data sets are loaded, we can calculate the districts' area for the population density.

<small><small>Remember: We reduced our sample to North Rhine Westphalia and dropped some variables.</small></small>

```r
# calculate area of districts
sf::st_area(sampling_area_districts_enhanced) %>% 
  head(4)
```

```
## Units: [m^2]
## [1] 1269653841 1991051810  797826703  694472630
```

```r
# areas will always be calculated
# in units according to the CRS 
sampling_area_districts_enhanced %>% 
  dplyr::mutate(area = sf::st_area(.)) %>% 
  dplyr::select(area)
```

```
## Simple feature collection with 138 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 4031313 ymin: 2684076 xmax: 4397808 ymax: 3337853
## Projected CRS: ETRS89-extended / LAEA Europe
## # A tibble: 138 × 2
##           area                                                                                geometry
##          [m^2]                                                                      <MULTIPOLYGON [m]>
##  1 1269653841. (((4323993 3203598, 4324507 3202914, 4324737 3202687, 4324920 3202646, 4325091 32023...
##  2 1991051810. (((4238609 3327711, 4238619 3327497, 4238662 3327236, 4238715 3327104, 4238688 33269...
##  3  797826703. (((4284723 3239656, 4284886 3239651, 4285048 3239787, 4285086 3239475, 4284857 32393...
##  4  694472630. (((4291874 3214346, 4291971 3214252, 4291991 3213730, 4292086 3213449, 4292213 32133...
##  5 1400950815. (((4265129 3310624, 4265143 3310552, 4265158 3310475, 4265181 3310400, 4265236 33102...
##  6  676259852. (((4272308 3259207, 4272316 3259144, 4272325 3259106, 4272330 3259076, 4272341 32590...
##  7  120191058. (((4186493 3247280, 4186547 3247144, 4187104 3247174, 4187249 3247185, 4187535 32472...
##  8 2883718972. (((4138763 3337307, 4138937 3337295, 4138977 3337314, 4138997 3337356, 4139001 33373...
##  9  982075046. (((4103569 3287402, 4103654 3287386, 4103715 3287396, 4103775 3287416, 4103848 32874...
## 10 2121108106. (((4177662 3290021, 4177688 3290019, 4177720 3290024, 4177783 3290037, 4177815 32900...
## # … with 128 more rows
```
]

---

## Population Density
All left to do is a simple mutation. Let's pipe it!

```r
# calculation population density
sampling_area_districts_enhanced <-
  sampling_area_districts_enhanced %>% 
  # calculate area 
  dplyr::mutate(area = sf::st_area(.)) %>% 
  # change unit to square kilometers
  dplyr::mutate(area_km2 = units::set_units
                    (area, km^2)) %>% 
  # recode variable as numeric
  dplyr::mutate(area_km2 = as.numeric
                    (area_km2)) %>% 
  # calculate population density
  dplyr::mutate(pop_dens = population/
           area_km2)
```
]

.pull-right[
<img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/unnamed-chunk-2-1.png" style="display: block; margin: auto;" />
]

---

## Aggregate Covid-19 Deaths

In order to calculate the number of Covid-19 deaths on the state level, we need to aggregate these numbers.

```r
# check data sets
sampling_area_districts_enhanced
```

```
## Simple feature collection with 138 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 4031313 ymin: 2684076 xmax: 4397808 ymax: 3337853
## Projected CRS: ETRS89-extended / LAEA Europe
## # A tibble: 138 × 10
##    district_id state                                                    geometry population death_rate death7_lk afd_voteshare_2…   area area_km2 pop_dens
##  * <chr>       <chr>                                          <MULTIPOLYGON [m]>      <dbl>      <dbl>     <dbl>            <dbl>  [m^2]    <dbl>    <dbl>
##  1 03155       nrw   (((4323993 3203598, 4324507 3202914, 4324737 3202687, 4324…     131772      0.345         0             8.03 1.27e9    1270.    104. 
##  2 03251       nrw   (((4238609 3327711, 4238619 3327497, 4238662 3327236, 4238…     218072      0.212         0             7.02 1.99e9    1991.    110. 
##  3 03252       nrw   (((4284723 3239656, 4284886 3239651, 4285048 3239787, 4285…     148580      0.362         1             8.88 7.98e8     798.    186. 
##  4 03255       nrw   (((4291874 3214346, 4291971 3214252, 4291991 3213730, 4292…      70207      0.657         0             9.04 6.94e8     694.    101. 
##  5 03256       nrw   (((4265129 3310624, 4265143 3310552, 4265158 3310475, 4265…     121645      0.513         0             8.62 1.40e9    1401.     86.8
##  6 03257       nrw   (((4272308 3259207, 4272316 3259144, 4272325 3259106, 4272…     158406      0.368         0             8.67 6.76e8     676.    234. 
##  7 03404       nrw   (((4186493 3247280, 4186547 3247144, 4187104 3247174, 4187…     164223      0.270         0             4.45 1.20e8     120.   1366. 
##  8 03454       nrw   (((4138763 3337307, 4138937 3337295, 4138977 3337314, 4138…     328930      0.238         0             6.09 2.88e9    2884.    114. 
##  9 03456       nrw   (((4103569 3287402, 4103654 3287386, 4103715 3287396, 4103…     137891      0.312         0             5.16 9.82e8     982.    140. 
## 10 03459       nrw   (((4177662 3290021, 4177688 3290019, 4177720 3290024, 4177…     359471      0.327         0             6.21 2.12e9    2121.    169. 
## # … with 128 more rows
```
]

```r
# check data sets
sampling_area
```

```
## Simple feature collection with 2 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 4031317 ymin: 2713678 xmax: 4357500 ymax: 3269987
## Projected CRS: ETRS89-extended / LAEA Europe
##   state                       geometry
## 1   nrw MULTIPOLYGON (((4031317 311...
## 2    bw MULTIPOLYGON (((4134155 273...
```
]

---

## Calculate Districts Within States

We rely here on the `sf` feature `st_join()` and the `dplyr` functions `group by()`and `summarize()`. First step is a join of two sf objects.
You always join based on the first-named object (x lying within which y?).

```r
# step one: district in
sf::st_join(sampling_area_districts_enhanced, sampling_area, join = st_within) %>% 
  head(.,2)
```

```
## Simple feature collection with 2 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 4205260 ymin: 3160220 xmax: 4332216 ymax: 3327915
## Projected CRS: ETRS89-extended / LAEA Europe
## # A tibble: 2 × 11
##   district_id state.x                                           geometry population death_rate death7_lk afd_voteshare_2…   area area_km2 pop_dens state.y
##   <chr>       <chr>                                   <MULTIPOLYGON [m]>      <dbl>      <dbl>     <dbl>            <dbl>  [m^2]    <dbl>    <dbl> <chr>  
## 1 03155       nrw     (((4323993 3203598, 4324507 3202914, 4324737 3202…     131772      0.345         0             8.03 1.27e9    1270.     104. <NA>   
## 2 03251       nrw     (((4238609 3327711, 4238619 3327497, 4238662 3327…     218072      0.212         0             7.02 1.99e9    1991.     110. <NA>
```

---

## A Word on Spatial Relations

Every time we want to use a spatial join, meaning matching two spatial objects based on their geometric relation, we need to think about the type of relation we assume.
`st_join` asks us to define the "join" type (default is intersect).

<img src="data:image/png;base64,#../img/spatial_relations.png" width="90%" style="display: block; margin: auto;" />
.center[
<small><small>https://www.e-education.psu.edu/maps/l2_p5.html</small></small>
]

---

## Group and Sum on State-Level

Back to our spatial join. After joining the information in which state each district lies (surprise: North Rhine Westphalia!), we can summarize the number of Covid-19 cases by grouping them on the state level.

```r
# second step: group & sum
sf::st_join(
  sampling_area_districts_enhanced, 
  sampling_area %>% dplyr::select(-state), 
  join = st_within) %>%
  dplyr::group_by(state) %>% 
  dplyr::summarize(death_rate_state = 
              mean(death_rate)) %>% 
  head(.,2)
```

```
## Simple feature collection with 2 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 4031313 ymin: 2684076 xmax: 4397808 ymax: 3337853
## Projected CRS: ETRS89-extended / LAEA Europe
## # A tibble: 2 × 3
##   state death_rate_state                                                                                geometry
##   <chr>            <dbl>                                                                      <MULTIPOLYGON [m]>
## 1 bw               0.443 (((4208884 2741153, 4208846 2741133, 4208764 2741085, 4208675 2741137, 4208548 27410...
## 2 nrw              0.440 (((4059941 3029719, 4059944 3029560, 4060029 3029491, 4060129 3029490, 4060141 30293...
```

---

# Final Steps

In the last step, we drop the geometries, re-join our variable back to the state data frame and calculate our aggregated Covid death rates. And - voilà - a simple function will give us our district differences.

```r
# the 'dplyr way' in completion
sampling_area_districts_enhanced <-
  sf::st_join(
    sampling_area_districts_enhanced,
    sampling_area %>% dplyr::select(-state), 
    join = st_within
  ) %>%
  dplyr::group_by(state) %>% 
  dplyr::summarize(
    death_rate_state = mean(death_rate)
  ) %>% 
  # already have geometries, so drop them
  sf::st_drop_geometry() %>% 
  # simple left_join
  dplyr::left_join(sampling_area_districts_enhanced, ., by = "state")

# calculate the difference between 
# state and district
sampling_area_districts_enhanced <-
  sampling_area_districts_enhanced %>% 
  mutate(death_diffs = death_rate - death_rate_state) 
```
]

.pull-right[
<img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/incidence-1.png" style="display: block; margin: auto;" />

]

---

## Respondents in Districts

We have population density and the difference in 7-day incidences on the district level. Since our analysis focuses on the individual-level, we can spatial join the information to our fake respondents' coordinates.

```r
# join back
spatial_information <-
  sampling_area_districts_enhanced %>%
  # keeping just the variables we want
  dplyr::select(district_id, pop_dens, death_diffs) %>% 
  # since we want to join district to
  # respondent defining coordintes first
  sf::st_join(fake_coordinates,
  # district data second
          . ,
    # some points may lie on the border
    # choosing intersects therefore
          join = st_intersects) %>% 
  # drop our coordinates for data protection
  sf::st_drop_geometry() 
```

---

## Respondents in Districts

```r
head(spatial_information, 5)
```

```
##         id_2 district_id pop_dens death_diffs
## 1 O7fTi57jEz       05758 555.9444 -0.08027408
## 2 GR3DU6RNwC       05758 555.9444 -0.08027408
## 3 HIROndK7fZ       08136 208.0870  0.05946759
## 4 r0II3cKoc6       05758 555.9444 -0.08027408
## 5 fffsx5Ka5Q       05958 132.1548 -0.12687836
```

---

## Distance to Closest Hospital

.pull-left[
We're getting a little bit more advanced here. Not in the means of spatial relations or calculations, but concerning data wrangling in general.
We got our survey respondents and already matched the population density and the difference of Corona incidences between state and district level.
We want to calculate the straight line distance between them and the closest hospital for each of our fake respondents.
]

.pull-right[
<img src="data:image/png;base64,#../img/distance.png" width="65%" style="display: block; margin: auto;" />
]

---

# Distance Calculation

`sf::st_distance()` will calculate between **all** respondents and **all** hospitals resulting in a matrix with 1,786,000 objects (1,000 respondent * 1,786 hospitals). We can make our lives a little bit easier by treating this matrix as a `tibble` or data frame having 1,000 observations with 1,786 variables.

```r
# distances between each respondent 
# and each hospital
distance_matrix <- 
  # point layer "distance from" 
  sf::st_distance(
    fake_coordinates, 
    # point layer "distance to"
    sampling_area_hospitals,
    # dense matrix with all 
    # pairwise distance
    by_element = FALSE
  ) %>% 
  # making life a little bit easier
  dplyr::as_tibble()

# check our matrix
# again, units = CRS units!
distance_matrix
```
]

```
## # A tibble: 1,000 × 1,786
##         V1      V2      V3      V4      V5     V6     V7     V8     V9    V10    V11    V12    V13    V14    V15    V16    V17    V18    V19    V20    V21
##        [m]     [m]     [m]     [m]     [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]    [m]
##  1 287791. 287902. 249823. 248798. 250631. 2.51e5 2.54e5 2.54e5 2.51e5 2.49e5 2.50e5 2.53e5 2.53e5 2.52e5 2.24e5 2.26e5 2.28e5 2.27e5 2.25e5 2.24e5 2.24e5
##  2 287430. 287548. 248395. 247451. 249297. 2.49e5 2.52e5 2.52e5 2.50e5 2.48e5 2.49e5 2.52e5 2.51e5 2.50e5 2.22e5 2.24e5 2.25e5 2.25e5 2.23e5 2.22e5 2.22e5
##  3 673225. 673426. 617898. 618634. 620619. 6.21e5 6.24e5 6.23e5 6.21e5 6.19e5 6.20e5 6.22e5 6.21e5 6.21e5 5.71e5 5.73e5 5.75e5 5.73e5 5.72e5 5.71e5 5.71e5
##  4 297790. 297917. 256840. 256062. 257934. 2.58e5 2.61e5 2.61e5 2.59e5 2.57e5 2.58e5 2.60e5 2.60e5 2.59e5 2.28e5 2.30e5 2.32e5 2.31e5 2.29e5 2.28e5 2.28e5
##  5 388291. 388389. 350424. 349535. 351388. 3.51e5 3.55e5 3.55e5 3.52e5 3.50e5 3.51e5 3.54e5 3.53e5 3.53e5 3.21e5 3.23e5 3.25e5 3.24e5 3.22e5 3.21e5 3.21e5
##  6 333300. 333455. 287259. 286952. 288881. 2.89e5 2.92e5 2.92e5 2.89e5 2.88e5 2.88e5 2.91e5 2.90e5 2.90e5 2.52e5 2.54e5 2.56e5 2.55e5 2.53e5 2.52e5 2.52e5
##  7 345534. 345633. 308252. 307270. 309109. 3.09e5 3.12e5 3.12e5 3.10e5 3.08e5 3.09e5 3.11e5 3.11e5 3.10e5 2.81e5 2.83e5 2.85e5 2.84e5 2.82e5 2.81e5 2.81e5
##  8 476950. 476996. 446288. 444964. 446737. 4.47e5 4.50e5 4.50e5 4.47e5 4.46e5 4.46e5 4.49e5 4.49e5 4.48e5 4.22e5 4.23e5 4.25e5 4.24e5 4.22e5 4.21e5 4.21e5
##  9 461612. 461655. 431762. 430372. 432132. 4.32e5 4.35e5 4.35e5 4.33e5 4.31e5 4.32e5 4.35e5 4.34e5 4.33e5 4.08e5 4.10e5 4.12e5 4.11e5 4.09e5 4.08e5 4.08e5
## 10 462088. 462136. 431349. 430020. 431792. 4.32e5 4.35e5 4.35e5 4.32e5 4.31e5 4.32e5 4.34e5 4.34e5 4.33e5 4.07e5 4.09e5 4.10e5 4.10e5 4.08e5 4.07e5 4.07e5
## # … with 990 more rows, and 1,765 more variables: V22 [m], V23 [m], V24 [m], V25 [m], V26 [m], V27 [m], V28 [m], V29 [m], V30 [m], V31 [m], V32 [m],
## #   V33 [m], V34 [m], V35 [m], V36 [m], V37 [m], V38 [m], V39 [m], V40 [m], V41 [m], V42 [m], V43 [m], V44 [m], V45 [m], V46 [m], V47 [m], V48 [m],
## #   V49 [m], V50 [m], V51 [m], V52 [m], V53 [m], V54 [m], V55 [m], V56 [m], V57 [m], V58 [m], V59 [m], V60 [m], V61 [m], V62 [m], V63 [m], V64 [m],
## #   V65 [m], V66 [m], V67 [m], V68 [m], V69 [m], V70 [m], V71 [m], V72 [m], V73 [m], V74 [m], V75 [m], V76 [m], V77 [m], V78 [m], V79 [m], V80 [m],
## #   V81 [m], V82 [m], V83 [m], V84 [m], V85 [m], V86 [m], V87 [m], V88 [m], V89 [m], V90 [m], V91 [m], V92 [m], V93 [m], V94 [m], V95 [m], V96 [m],
## #   V97 [m], V98 [m], V99 [m], V100 [m], V101 [m], V102 [m], V103 [m], V104 [m], V105 [m], V106 [m], V107 [m], V108 [m], V109 [m], V110 [m], V111 [m],
## #   V112 [m], V113 [m], V114 [m], V115 [m], V116 [m], V117 [m], V118 [m], V119 [m], V120 [m], V121 [m], …
```
]

---

## Find Minimum Distance

That's all there is concerning the "spatial" part of our data wrangling. 
From now on, just good old (boring) data crunching to get our distance to the closest hospital.

```r
distance_closest <-
    distance_matrix %>% 
    # from unit to numeric
    dplyr::mutate_all(as.numeric) %>% 
    # identify for each row the minimum
    # & save in variable
    dplyr::mutate(dist_closest_hospital = 
                  (apply(., 1, min))) %>%  
    # select only column 
    # containing smallest distance
    dplyr::select(dist_closest_hospital)
```
]

```
## # A tibble: 1,000 × 1
##    dist_closest_hospital
##                    <dbl>
##  1                 7177.
##  2                 5119.
##  3                10660.
##  4                 6080.
##  5                10661.
##  6                17613.
##  7                 7589.
##  8                 5165.
##  9                 6401.
## 10                 7724.
## # … with 990 more rows
```

]

---

## Get all the information together!

Again, I prefer to work with kilometers rather than meters.
And I want to add our new variable to the other spatial information we already prepared.
Luckily, I know that the spatial information table has the same length and order as the fake coordinates. Otherwise, it would be wise to bind via the original coordinate data frame we used for `sf::st_distance()`.

```r
spatial_information <-
  distance_closest %>% 
  # get kilometer
  dplyr::mutate(dist_closest_hospital = dist_closest_hospital/1000 ) %>% 
  # bind columns with spatial information
  # only bind with other data set than the
  # original coordinates when you are 100
  # percent sure it's same length and order!
  dplyr::bind_cols(spatial_information, .) 
```
]

```
##           id_2 district_id   pop_dens   death_diffs dist_closest_hospital
## 1   O7fTi57jEz       05758  555.94443 -0.0802740755             7.1769192
## 2   GR3DU6RNwC       05758  555.94443 -0.0802740755             5.1187058
## 3   HIROndK7fZ       08136  208.08695  0.0594675932            10.6603226
## 4   r0II3cKoc6       05758  555.94443 -0.0802740755             6.0797974
## 5   fffsx5Ka5Q       05958  132.15479 -0.1268783629            10.6610723
## 6   q46L4y6sHb       05762  116.36963  0.0448750538            17.6126863
## 7   vEMkSbk41Y       05754  376.26479 -0.0900035581             7.5894187
## 8   QbFp2Km7CN       05382  520.98731  0.0181760619             5.1651725
## 9   xPovR3CEG5       05382  520.98731  0.0181760619             6.4013152
## 10  YFL4QpbWjx       05382  520.98731  0.0181760619             7.7236223
## 11  5Yso5RnugO       05162  783.82839 -0.0405731509             4.9733343
## 12  M547iBtNyh       08425  146.01760 -0.1069676028             4.5428958
## 13  w5d6fAeWTm       05124 2107.60149  0.0814873748             3.4912852
## 14  WxFnIGdqcD       08437  108.70002 -0.1206711475             3.6645189
## 15  yMMAdqn7lL       05366  155.45360  0.0481721321             4.2989037
## 16  g5wcDKWuCz       08127  133.33031  0.0483465435            14.3985737
## 17  jLtHcdZJxt       08215  412.04059  0.0096710668             8.9447827
## 18  i9e4AgqHU9       08115  636.57455 -0.1033003928             5.3878743
## 19  q3eKUrwWbK       08236  348.61600  0.0368877917             0.9011041
## 20  Yl8PcrMvh3       08326  207.49459  0.0229200627             8.0975778
## 21  dSoRjVOE7u       08436  175.19027 -0.2306501247            12.6256103
## 22  ILixjNNfI5       08237  135.91128  0.1117571667             8.5548379
## 23  Q8Gt7HL1Hm       05958  132.15479 -0.1268783629            14.8147212
## 24  ij7sGLQ7oG       08128  101.68632 -0.0143387664             8.5097311
## 25  tjMWkLxa8L       05170  441.42773 -0.1175255084             1.3915677
## 26  TnWWR2gV3M       05954  786.40592  0.1635924196             1.1605018
## 27  4A8iTh6QQh       08116  834.10623 -0.0045280880             0.6257304
## 28  RjSCbEu2Vc       08235  200.80118  0.0684648164            13.6047285
## 29  sjFjDL4MZI       05116 1521.83571 -0.0312585218             3.0356690
## 30  vqw8tYzkkz       08136  208.08695  0.0594675932             6.7889479
## 31  TVfzqhKIaE       08436  175.19027 -0.2306501247             8.7618815
## 32  3Uq1ZMs5QB       08216  314.15154  0.0031697810             3.0404315
## 33  AfNswvlgPy       08416  439.44729 -0.1206246543             2.2502235
## 34  QDmoFP8X7R       05754  376.26479 -0.0900035581             6.8249624
## 35  vYLTl2ga8w       08136  208.08695  0.0594675932             9.3466382
## 36  6TFTwHjI4G       05566  249.62364 -0.1455078327             3.7749312
## 37  hUzBKNXfCZ       08425  146.01760 -0.1069676028             7.5857917
## 38  03mdYtCgXf       05166  529.70024 -0.0287075384             2.7771277
## 39  b3Hk0t2gPr       05334  790.16586  0.0359856571             0.9483628
## 40  XKIq3yQUG9       05513 2460.64463  0.2967109665             2.7474021
## 41  4VqqaM5jtu       05566  249.62364 -0.1455078327             8.8303838
## 42  TUfS6aBAJW       05358  281.92118  0.0121631341             8.6313366
## 43  5Ns4ElbtSz       08436  175.19027 -0.2306501247             4.4271156
## 44  kpdCocyxRk       08136  208.08695  0.0594675932             3.3811249
## 45  720gcmF7wL       05754  376.26479 -0.0900035581            12.1317325
## 46  0czRW8CI3y       05358  281.92118  0.0121631341             4.5362033
## 47  n8qH3Al3ic       05915  785.90473  0.0081990340             1.6337306
## 48  xyXh9oCMDQ       08416  439.44729 -0.1206246543             7.8224508
## 49  59lwaKLnNJ       05566  249.62364 -0.1455078327             8.1967527
## 50  w2tjGrLdKo       08125  315.27257 -0.1730679985            13.9048089
## 51  GjRqWOT11e       08325  182.15285  0.0702995398             3.9516944
## 52  15JyELj7ZA       05154  254.23646 -0.0258392971             3.8286432
## 53  JnYEbhRS95       08127  133.33031  0.0483465435             9.3547712
## 54  f3gxkmeLQ1       05362  668.01880 -0.0898995794             0.9391593
## 55  IVc9JffkNB       08315  192.26363 -0.0687592509            10.3015608
## 56  z281LoGalB       05962  385.28014  0.0071737291             3.0696118
## 57  nhhIRJHvos       08415  263.21878  0.0004831449            10.0949932
## 58  ML4KcF7yWH       08435  327.56409  0.0021880157            13.7477861
## 59  iiHHR7JyHJ       05774  247.29856 -0.1955966928            12.2749802
## 60  76k4DMP45N       05966  187.32643  0.0768122765             4.3314970
## 61  V7yvuXm4Hy       05566  249.62364 -0.1455078327             1.8001089
## 62  lvTmcOJjfg       08125  315.27257 -0.1730679985             2.1538365
## 63  8dmcLTjbha       08336  283.51661  0.1201763290             9.5131615
## 64  VKLrCk41J4       05154  254.23646 -0.0258392971             5.4475335
## 65  HIexK5hPlk       05913 2101.41275 -0.1527398270             0.4997071
## 66  UFcOYNRd5q       08226  516.79121 -0.0478656131             1.4622856
## 67  WsWSMzi5ze       08425  146.01760 -0.1069676028             7.4922686
## 68  sh7hqR4Mcu       05766  278.50287  0.1156888380             7.7892734
## 69  hCFbC7GqFc       08128  101.68632 -0.0143387664             8.1348111
## 70  gHZzGCtOwl       05762  116.36963  0.0448750538            10.9278658
## 71  eNQIdNSMcB       08417  206.97038 -0.0900173449             4.2638120
## 72  p6BrrTljsD       05754  376.26479 -0.0900035581             5.8957855
## 73  nzcQz56dRW       08326  207.49459  0.0229200627            10.0613726
## 74  Dkm5QTJAp9       08337  151.42175  0.1192169854             7.1484623
## 75  Kjaej1ByRh       05770  269.32985 -0.0633469583            15.5539863
## 76  cESKq5A92M       08125  315.27257 -0.1730679985             5.1950324
## 77  MJb0C1ntlw       05566  249.62364 -0.1455078327             4.2111722
## 78  ZDoEFh1aTI       05558  198.42971 -0.2688506462             4.3143844
## 79  y9iP0O6Umw       05558  198.42971 -0.2688506462             7.1715847
## 80  udztRUoW3L       05958  132.15479 -0.1268783629            16.9414143
## 81  tYKrEYIYoZ       05966  187.32643  0.0768122765            16.3213389
## 82  zoVxlfThxB       05770  269.32985 -0.0633469583            16.9334244
## 83  v0RaBBmUss       08435  327.56409  0.0021880157             5.7394218
## 84  QfHtu8PqVJ       05911 2516.36577 -0.0132357586             3.9081223
## 85  M78fbvuTib       05119 2704.54500  0.3976744385             6.3139059
## 86  1aOtERytqO       05774  247.29856 -0.1955966928             9.2289184
## 87  SEuAXh5TKv       08117  402.67820 -0.0018583774             5.5312932
## 88  fLboeZ2cAQ       08337  151.42175  0.1192169854             6.3590251
## 89  PHBYrB7Z0w       05370  408.82725  0.3059996213             5.5006891
## 90  wqzivihhEB       08327  193.01362  0.0463484407            10.5397453
## 91  c5tYqZfwo8       08436  175.19027 -0.2306501247             2.2968151
## 92  NsmRK2LH5B       05166  529.70024 -0.0287075384             2.0910968
## 93  gzXmCFKtbV       05154  254.23646 -0.0258392971             6.2214363
## 94  5RNEkSWaRj       08116  834.10623 -0.0045280880             3.8910856
## 95  OMiDIljHbi       05334  790.16586  0.0359856571             6.1887027
## 96  hxefs2RWDi       08435  327.56409  0.0021880157             8.8376278
## 97  918PuIkBnE       05962  385.28014  0.0071737291             9.9172968
## 98  fmIHMQrA5X       05978  725.16111  0.1246544538             2.8218814
## 99  QrWev484Vv       08117  402.67820 -0.0018583774             4.4173467
## 100 lTUzPRIgBP       05378  646.60085 -0.1647324428             8.4575022
## 101 BnV2FTfHb5       08317  232.59748  0.0665610848             5.1952035
## 102 mQj70lFDey       08116  834.10623 -0.0045280880             2.3024657
## 103 k4AR1IemmA       08225  127.72837 -0.0434328301             4.7535319
## 104 qkSGULBLnK       05154  254.23646 -0.0258392971             7.3747300
## 105 QBV1IrQFIE       05382  520.98731  0.0181760619             7.6896369
## 106 eLtuhFMOH4       05754  376.26479 -0.0900035581            12.5654488
## 107 QD1T8zrZD4       08317  232.59748  0.0665610848             4.6987275
## 108 sG2fAbgduS       05762  116.36963  0.0448750538            19.3802048
## 109 WKMr5VpGYt       08336  283.51661  0.1201763290             8.2247506
## 110 L0CL9HhTSg       05366  155.45360  0.0481721321             1.4173391
## 111 QtIPVZDhpB       05962  385.28014  0.0071737291             9.4086334
## 112 UOsDIh1aqd       08336  283.51661  0.1201763290             0.9728203
## 113 wqETvNjB0K       05374  295.89374  0.0241034281             3.1561608
## 114 Q28RqEZgKa       08317  232.59748  0.0665610848             8.5830843
## 115 LPJd2in5TC       05766  278.50287  0.1156888380             6.9948860
## 116 vdHrfJ9yyt       08125  315.27257 -0.1730679985             1.7568195
## 117 KRMlkJVPXp       08136  208.08695  0.0594675932             9.2101894
## 118 tBRAdDjuIc       05334  790.16586  0.0359856571             1.1693557
## 119 a9i7MxThm8       08335  351.04546 -0.0050070137             3.4061744
## 120 FUGL7cdw66       05762  116.36963  0.0448750538             3.3444580
## 121 Y7yI7MiJbK       08235  200.80118  0.0684648164            14.6907684
## 122 EbPbrUvyFy       08125  315.27257 -0.1730679985             7.5519700
## 123 vO5mufgmLa       08317  232.59748  0.0665610848             7.7042075
## 124 T0M823AdgM       08436  175.19027 -0.2306501247            10.4517857
## 125 CQG7V6J2nB       05958  132.15479 -0.1268783629             9.9436876
## 126 Uukl9d3gNm       08127  133.33031  0.0483465435            12.7117475
## 127 5bWAOPlIi6       08421 1060.26770 -0.1866207154             8.9441418
## 128 TgDlsOqy9U       08337  151.42175  0.1192169854             4.1594269
## 129 aqkexBt2hM       08336  283.51661  0.1201763290            12.2737898
## 130 RYZvuNoYDw       08315  192.26363 -0.0687592509             3.3260611
## 131 dsn83ArNC6       08417  206.97038 -0.0900173449             3.7295990
## 132 l5FIs6YoCy       05315 2663.66608 -0.1617247369             1.1657705
## 133 OlKaexqes0       05358  281.92118  0.0121631341             5.3138192
## 134 2EvAnMCjfJ       05762  116.36963  0.0448750538            13.5918381
## 135 73RTkvqSkB       08426  143.45161 -0.0776801790             9.5404686
## 136 wZ8MjLy6lu       05774  247.29856 -0.1955966928            13.5231612
## 137 SDkNqa4Dwy       08326  207.49459  0.0229200627             5.3779418
## 138 NvTuj4xcF3       05566  249.62364 -0.1455078327             7.6271905
## 139 J7jIc27oJk        <NA>         NA            NA             9.1724279
## 140 sdo7WEwJ6K       05970  242.93087 -0.1534861414            10.0698918
## 141 Y1v77mcfID       05111 2853.61446  0.0018459746             0.3594995
## 142 FgRX1nkVzG       05766  278.50287  0.1156888380            11.2889831
## 143 hzrm3hbPWB       05962  385.28014  0.0071737291             6.2940172
## 144 VxM3OsOZk2       08117  402.67820 -0.0018583774            13.3266676
## 145 c5Nne81No8       08117  402.67820 -0.0018583774            12.7618121
## 146 Kg1sMgzHNT       08317  232.59748  0.0665610848             3.7538745
## 147 qd6L4MdMQ7       08215  412.04059  0.0096710668             4.8623859
## 148 YbKhgmhcgK       05566  249.62364 -0.1455078327             7.8514655
## 149 RwMWpsfWUq       08226  516.79121 -0.0478656131             8.8318219
## 150 9CSUu9RYAi       05374  295.89374  0.0241034281            12.2868468
## 151 9LHHB7aHKf       05566  249.62364 -0.1455078327             4.0367246
## 152 F4L5wWpFV5       08226  516.79121 -0.0478656131             9.4243638
## 153 dHanLCqkPs       05554  261.65165 -0.1496587346             6.4894109
## 154 JcQFgQJsft       05770  269.32985 -0.0633469583             4.9788130
## 155 1IUPL6Al58       05978  725.16111  0.1246544538             4.8752218
## 156 dsxQdzHkcz       08436  175.19027 -0.2306501247             4.2416221
## 157 hMfiwAByhG       08237  135.91128  0.1117571667             6.7739425
## 158 zVUemE7njg       08222 2134.32358  0.0106373029             2.4518424
## 159 uq9TZQliVD       05766  278.50287  0.1156888380             9.5419829
## 160 DJPfgldy5V       08435  327.56409  0.0021880157             3.0370179
## 161 YY3PT4itcG       05122 1788.76737  0.0644885912             0.7414913
## 162 mnSKbzEJi5       05958  132.15479 -0.1268783629             5.6040007
## 163 Tw6KaqugsI       08326  207.49459  0.0229200627             9.1088834
## 164 CszoAqWtOa       05754  376.26479 -0.0900035581             5.2575322
## 165 0MLELq0a2N       05774  247.29856 -0.1955966928             7.4105020
## 166 Q7qJjcP6UO       08136  208.08695  0.0594675932             4.3154142
## 167 6aoY3IBQn3       05762  116.36963  0.0448750538             8.1509789
## 168 t5OcE1i2SX       08237  135.91128  0.1117571667             1.9753395
## 169 Bu47sWZ95i       08315  192.26363 -0.0687592509             6.3856617
## 170 u5mewoG6Ta       08119  498.38193 -0.0395572763             7.3830039
## 171 H2MCgBo58p       08231 1282.46450  0.2530660691             1.6795341
## 172 LpKqif65dW       05774  247.29856 -0.1955966928            13.7915798
## 173 brmgzpVpGN       05754  376.26479 -0.0900035581             8.9290701
## 174 hLHw63lxOj       08231 1282.46450  0.2530660691             1.4666768
## 175 Ay7sbbONtS       05774  247.29856 -0.1955966928             8.9877009
## 176 jPO7dtpycU       08425  146.01760 -0.1069676028            10.9914236
## 177 wFXdskQLBC       08115  636.57455 -0.1033003928             4.2425936
## 178 mhXMx1UoaN       08436  175.19027 -0.2306501247            11.2780310
## 179 mrceF8L1p0       08119  498.38193 -0.0395572763             2.6276322
## 180 eM34tQbKEj       08126  145.10239 -0.0107632803            19.5265865
## 181 a9rTREVr7a       08336  283.51661  0.1201763290             2.0119247
## 182 FYouyvAlYA       05358  281.92118  0.0121631341             5.9350272
## 183 tmDnYssZ8p       08235  200.80118  0.0684648164            11.5431368
## 184 RlWw4z4Aal       05774  247.29856 -0.1955966928            10.6843399
## 185 uAXKj3BsMz       05554  261.65165 -0.1496587346             8.6082976
## 186 RYtPyiXyrL       08135  211.68600  0.1612797011             6.2065619
## 187 Bg134QEfOG       05754  376.26479 -0.0900035581             5.2467802
## 188 rQAGQVY5P1       05554  261.65165 -0.1496587346             6.2315338
## 189 khk2y7TEOM       08316  245.24450 -0.0292549563             7.8921012
## 190 KKL2kkRUsR       08115  636.57455 -0.1033003928             6.3038816
## 191 taLHx0TkVN        <NA>         NA            NA             6.3294839
## 192 wO4RMPqwwY       05570  210.39111 -0.0872441520             6.2193234
## 193 cMGzbattzV       08327  193.01362  0.0463484407             3.1805612
## 194 JNz5FC4TAw       08116  834.10623 -0.0045280880             4.4610096
## 195 sbL9nCmGpU       09575   79.86946 -0.0456319148             9.4660969
## 196 e9ZNlahuYA       08425  146.01760 -0.1069676028            11.9815884
## 197 VujtUgryy2       08216  314.15154  0.0031697810             6.0583587
## 198 UMGUve8RAo       08226  516.79121 -0.0478656131             6.6430202
## 199 TblyqAb4iv       08126  145.10239 -0.0107632803            12.2779588
## 200 BXYGMJqfuB       08425  146.01760 -0.1069676028             9.5190939
##  [ reached 'max' / getOption("max.print") -- omitted 800 rows ]
```
]

---

## Immigrant Rate Buffers

...and we're not yet done: we still need the immigrant rate in the neighborhood. Let's calculate buffers of 500 meters and add their mean values to our dataset.

```r
# download data & create rate
immigrants <- 
  z11::z11_get_100m_attribute(STAATSANGE_KURZ_2)

inhabitants <-
  z11::z11_get_100m_attribute(Einwohner) %>% 
  terra::resample(immigrants)
                              
immigrant_rate <- immigrants / inhabitants * 100

# set missings
immigrant_rate[is.na(immigrant_rate)] <- 0

immigrant_buffers <- 
  terra::extract(
    immigrant_rate,
    terra::vect(fake_coordinates),
    buffer = 500,
    fun = mean
  )

# spatially link with buffers on the fly
spatial_information <-
  spatial_information %>% 
  dplyr::mutate(immigrant_buffers = immigrant_buffers[[2]])
```

---

## Join with Fake Burden Score

I hope you're not tired to join data tables.
Since we care a tiny bit more about data protection than others, we have yet another joining task left: joining the information we received using our (protected) fake coordinates to the actual survey data via the correspondence table.

```r
# last joins for now
fake_survey_data_spatial <-
  # first join the id
  dplyr::left_join(
    correspondence_table, 
    spatial_information, 
    by = "id_2"
  ) %>% 
  # drop the fake_coordinate id
  dplyr::select(-id_2) %>% 
  # join the survey data
  dplyr::left_join(
    fake_survey_data,
    by = "id"
  ) 
```
]

.pull-right[
<img src="data:image/png;base64,#2_2_Applied_Data_Wrangling_files/figure-html/correlation-plot-1.png" width="75%" style="display: block; margin: auto;" />

]

---

## ... and when things get complicated?

In these presented examples, things are working out quite well. However, you will experience that sometimes things can get a little more complicated. Challenges like not matching geometries cause problems for simple spatial joins and force you to decide how to crop the information. For example, ZIP codes and electoral districts do not match with the administrative districts in Germany.

You might also want to advance your measurements and dive deeper into the measurements like densities, distances to lines or polygons, spatial autocorrelation measurements, and neighborhood matrices. 
All of this is possible using *R/RStudio*!

---

[Exercise](https://stefanjuenger.github.io/gis_socsci_konstanz/exercises/2_2_1_Spatial_Joins_OPTIONAL.html)

[Solution](https://stefanjuenger.github.io/gis_socsci_konstanz/exercises/2_2_1_Spatial_Joins_OPTIONAL.html)

---

layout: false
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background-size: cover

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]
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